code: def convert_dpt_checkpoint(
      model_name, pytorch_dump_folder_path, push_to_hub, verify_logits
  ):
      """
      Copy/paste/tweak model's weights to our DPT structure.
      """
  
      # define DPT configuration based on URL
      checkpoint_url = name_to_url[model_name]
      config = get_dpt_config(model_name)
  
      # load original DPT state_dict from URL
      print("URL:", checkpoint_url)
      dpt_state_dict = torch.hub.load_state_dict_from_url(
          checkpoint_url, map_location="cpu"
      )["state_dict"]
      # rename keys
      rename_keys = create_rename_keys_dpt(config)
      for src, dest in rename_keys:
          rename_key(dpt_state_dict, src, dest)
  
      # load original backbone state_dict from URL
      if "small" in model_name:
          original_model = torch.hub.load(
              "facebookresearch/dinov2", "dinov2_vits14"
          )
      elif "base" in model_name:
          original_model = torch.hub.load(
              "facebookresearch/dinov2", "dinov2_vitb14"
          )
      elif "large" in model_name:
          original_model = torch.hub.load(
              "facebookresearch/dinov2", "dinov2_vitl14"
          )
      elif "giant" in model_name:
          original_model = torch.hub.load(
              "facebookresearch/dinov2", "dinov2_vitg14"
          )
      else:
          raise NotImplementedError("To do")
      original_model.eval()
      backbone_state_dict = original_model.state_dict()
  
      # rename keys
      rename_keys = create_rename_keys_backbone(config)
      for src, dest in rename_keys:
          rename_key(backbone_state_dict, src, dest)
  
      # read in qkv matrices
      read_in_q_k_v(backbone_state_dict, config)
  
      for key, val in backbone_state_dict.copy().items():
          val = backbone_state_dict.pop(key)
          if "w12" in key:
              key = key.replace("w12", "weights_in")
          if "w3" in key:
              key = key.replace("w3", "weights_out")
          backbone_state_dict[key] = val
  
      # merge state_dicts
      state_dict = {**backbone_state_dict, **dpt_state_dict}
  
      # load HuggingFace model
      model = DPTForDepthEstimation(config)
      missing_keys, unexpected_keys = model.load_state_dict(
          state_dict, strict=False
      )
      print("Missing keys:", missing_keys)
      print("Unexpected keys:", unexpected_keys)
      assert missing_keys == [
          "neck.fusion_stage.layers.0.residual_layer1.convolution1.weight",
          "neck.fusion_stage.layers.0.residual_layer1.convolution2.weight",
      ]
      model.eval()
  
      # Verify image processor
      processor = DPTImageProcessor(
          do_resize=False,
          do_rescale=False,
          do_pad=True,
          size_divisor=14,
          do_normalize=True,
          image_mean=(123.675, 116.28, 103.53),
          image_std=(58.395, 57.12, 57.375),
      )
  
      image = prepare_img()
      pixel_values = processor(
          image, return_tensors="pt"
      ).pixel_values.float()
      original_pixel_values = get_original_pixel_values(image)
  
      assert torch.allclose(pixel_values, original_pixel_values)
  
      # Verify forward pass
      with torch.no_grad():
          outputs = model(pixel_values)
  
      predicted_depth = outputs.predicted_depth
  
      print("Shape of predicted depth:", predicted_depth.shape)
      print(
          "First values of predicted depth:", predicted_depth[0, :3, :3]
      )
  
      # assert logits
      if verify_logits:
          if model_name == "dpt-dinov2-small-nyu":
              expected_shape = torch.Size([1, 576, 736])
              expected_slice = torch.tensor(
                  [
                      [3.3576, 3.4741, 3.4345],
                      [3.4324, 3.5012, 3.2775],
                      [3.2560, 3.3563, 3.2354],
                  ]
              )
  
          assert predicted_depth.shape == torch.Size(expected_shape)
          assert torch.allclose(
              predicted_depth[0, :3, :3], expected_slice, atol=1e-5
          )
          print("Looks ok!")
  
      if pytorch_dump_folder_path is not None:
          Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
          print(
              f"Saving model and processor to {pytorch_dump_folder_path}"
          )
          model.save_pretrained(pytorch_dump_folder_path)
          processor.save_pretrained(pytorch_dump_folder_path)
  
      if push_to_hub:
          print("Pushing model and processor to hub...")
          model.push_to_hub(repo_id=f"facebook/{model_name}")
          processor.push_to_hub(repo_id=f"facebook/{model_name}")
  
file_path: transformers/src/transformers/models/dpt/convert_dinov2_depth_to_hf.py
imports: import argparse
  import itertools
  import math
  from pathlib import Path
  import requests
  import torch
  from PIL import Image
  from torchvision import transforms
  from transformers import Dinov2Config, DPTConfig, DPTForDepthEstimation, DPTImageProcessor
  from transformers.utils import logging
label_desc: Use image node_id 8 for calling a global function with example usage: convert_dpt_checkpoint(model_name, pytorch_dump_folder_path, push_to_hub, verify_logits) without return types